Gender-specific patterns in the artificial intelligence scientific ecosystem |
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| Institution: | 1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 2W1 Canada;2. National Research Council Canada, Montreal, QC H3T 2B2 , Canada;1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 2W1 Canada;2. National Research Council Canada, Montreal, QC H3T 2B2 , Canada;1. Hasselt University, Belgium;2. KU Leuven, Belgium, University of Antwerp, Belgium;1. Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany;2. German Centre for Higher Education Research and Science Studies (DZHW), Berlin, Germany;3. Department of Social and Political Sciences, University of Milan, Milan, Italy;1. Department of Statistics, Cochin University of Science and Technology, Cochin 682022, India;2. Department of Statistics, Government Arts College, Thiruvananthapuram 695014, India |
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| Abstract: | Gender disparity in science is one of the most focused debating points among authorities and the scientific community. Over the last few decades, numerous initiatives have endeavored to accelerate gender equity in academia and research society. However, despite the ongoing efforts, gaps persist across the world, and more measures need to be taken. Using social network analysis, natural language processing, and machine learning, in this study, we comprehensively analyzed gender-specific patterns in the highly interdisciplinary and evolving field of artificial intelligence for the period of 2000–2019. Our findings suggest an overall increasing rate of mixed-gender collaborations. From the observed gender-specific collaborative patterns, the existence of disciplinary homophily at both dyadic and team levels is confirmed. However, a higher preference was observed for female researchers to form homophilous collaborative links. Our core-periphery analysis indicated a significant positive association between having diverse collaboration and scientific performance and experience. We found evidence in support of expecting the rise of new female superstar researchers in the artificial intelligence field. |
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